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""" |
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Created on Wed Nov 23 06:07:56 2022 |
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@author: limei |
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""" |
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import gradio as gr |
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import numpy as np |
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from PIL import Image |
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import requests |
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import hopsworks |
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import joblib |
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project = hopsworks.login() |
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fs = project.get_feature_store() |
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mr = project.get_model_registry() |
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model = mr.get_model("titanic_modal", version=6) |
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model_dir = model.download() |
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model = joblib.load(model_dir + "/titanic_model.pkl") |
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def titanic(pclass, sex, fare, embarked, familysize, family, appellation, cabin): |
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input_list = [] |
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if pclass == "1": |
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input_list.extend([1,0,0]) |
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elif pclass == "2": |
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input_list.extend([0,1,0]) |
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elif pclass == "3": |
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input_list.extend([0,0,1]) |
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if sex == "Male": |
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input_list.append(0) |
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else: |
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input_list.append(1) |
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input_list.append(fare) |
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if embarked == "S": |
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input_list.extend([1,0,0]) |
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elif embarked == "C": |
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input_list.extend([0,1,0]) |
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elif embarked == "Q": |
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input_list.extend([0,0,1]) |
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input_list.append(familysize) |
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if family == "Family_Single": |
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input_list.extend([1,0,0]) |
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elif family == "Family_Small": |
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input_list.extend([0,1,0]) |
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elif family == "Family_Large": |
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input_list.extend([0,0,1]) |
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if appellation == "master": |
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input_list.extend([1,0,0,0,0,0]) |
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elif appellation == "miss": |
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input_list.extend([0,1,0,0,0,0]) |
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elif appellation == "mr": |
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input_list.extend([0,0,1,0,0,0]) |
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elif appellation == "mrs": |
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input_list.extend([0,0,0,1,0,0]) |
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elif appellation == "officer": |
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input_list.extend([0,0,0,0,1,0]) |
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elif appellation == "royalty": |
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input_list.extend([0,0,0,0,0,1]) |
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if cabin == "A": |
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input_list.extend([1,0,0,0,0,0,0,0,0]) |
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elif cabin == "B": |
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input_list.extend([0,1,0,0,0,0,0,0,0]) |
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elif cabin == "C": |
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input_list.extend([0,0,1,0,0,0,0,0,0]) |
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elif cabin == "D": |
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input_list.extend([0,0,0,1,0,0,0,0,0]) |
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elif cabin == "E": |
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input_list.extend([0,0,0,0,1,0,0,0,0]) |
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elif cabin == "F": |
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input_list.extend([0,0,0,0,0,1,0,0,0]) |
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elif cabin == "G": |
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input_list.extend([0,0,0,0,0,0,1,0,0]) |
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elif cabin == "T": |
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input_list.extend([0,0,0,0,0,0,0,1,0]) |
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else: |
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input_list.extend([0,0,0,0,0,0,0,0,1]) |
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res = model.predict(np.asarray(input_list).reshape(1, -1)) |
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res = res.astype(int) |
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titanic_url = "https://raw.githubusercontent.com/M75583/Machinelearning/main/" + str(res[0]) + ".png" |
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img = Image.open(requests.get(titanic_url, stream=True).raw) |
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return img |
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demo = gr.Interface( |
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fn=titanic, |
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title="Titanic Predictive Analytics", |
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description="Experiment with titanic dataset values.", |
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allow_flagging="never", |
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inputs=[ |
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gr.Dropdown(choices=["1", "2", "3"], label="PClass", value="1"), |
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gr.Radio(choices=["Male", "Female"], label="Gender", value="Male"), |
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gr.inputs.Number(default=40.99, label="Fare"), |
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gr.Dropdown(choices=["S","C","Q"], label="Embarked", value="S"), |
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gr.Number(label="Family Size", precision=0, value=1), |
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gr.Dropdown(choices=["Family_Single","Family_Small","Family_Large"], label="Family", value="Family_Single"), |
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gr.Dropdown(choices=["master", "miss", "mr", "mrs", "officer", "royalty"], label="Appellation", value="master"), |
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gr.Dropdown(choices=["A", "B", "C", "D", "E", "F", "G", "T", "U"], label="Cabin", value="A"), |
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], |
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outputs=gr.Image(type="pil")) |
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demo.launch() |
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